About this Research Topic
Human-robot interaction and collaboration is a major topic in human-coupled robotic systems, which allows the robot to understand and cooperate with the human. In the design of traditional human-robot interactions, physical human-robot interactions have gained considerable interests for robust operating of robotic systems. With the development of neural technologies, neural interface has been implemented for achieving natural human-robot interaction and collaboration, which makes the human operate the robotic system more conveniently. This kind of interactions can be also called cognitive human-robot interaction. Neural interfaces of cognitive human-robot interaction and collaboration are widely used in human-coupled robotic systems in many different domains, such as rehabilitation, entertainment and so on.
The new generation of human-robot interaction technologies are supposed to enhance cognitive abilities of human-coupled robotic systems, which aiming to achieve natural interaction and collaboration modes between human and robots. Cognitive human-robot interactions include brain-machine interface with electroencephalographic (EEG), muscle signals with electromyography (EMG), eye-tracking interactions, gesture and so on. Neural interfaces of cognitive human-robot interaction with neural signals (e.g. EEG, EMG et al.) can obtain the intention of human more directly. However, how to achieve robust interaction with neural interfaces is a challenge for cognitive human-robot interaction. With robust interactions based on neural interfaces, another important issue for human-coupled robotic systems is how to collaborate with human naturally.
This research topic is therefore proposed to promote the fundamentals and technologies of cognitive human-robot interaction with neural interfaces for human-coupled robotic systems, which includes but not limited to:
Topics of Interest:
• Design of neural interfaces for human-robot interactions
• Brain-machine interfaces for cognitive interaction
• Multi-modal sensory integration of neural signals
• Fundamentals of human-robot collaboration for cognition
• Neural control for human-coupled robotic systems
• Interactive learning for robust human-robot interaction
• Biomechanical modelling and analysis of human-coupled robotic systems
• Human-robot interaction applications in human-coupled robotic systems
• Cognitive human-robot collaboration applications for rehabilitation
The new generation of human-robot interaction technologies are supposed to enhance cognitive abilities of human-coupled robotic systems, which aiming to achieve natural interaction and collaboration modes between human and robots. Cognitive human-robot interactions include brain-machine interface with electroencephalographic (EEG), muscle signals with electromyography (EMG), eye-tracking interactions, gesture and so on. Neural interfaces of cognitive human-robot interaction with neural signals (e.g. EEG, EMG et al.) can obtain the intention of human more directly. However, how to achieve robust interaction with neural interfaces is a challenge for cognitive human-robot interaction. With robust interactions based on neural interfaces, another important issue for human-coupled robotic systems is how to collaborate with human naturally.
This research topic is therefore proposed to promote the fundamentals and technologies of cognitive human-robot interaction with neural interfaces for human-coupled robotic systems, which includes but not limited to:
Topics of Interest:
• Design of neural interfaces for human-robot interactions
• Brain-machine interfaces for cognitive interaction
• Multi-modal sensory integration of neural signals
• Fundamentals of human-robot collaboration for cognition
• Neural control for human-coupled robotic systems
• Interactive learning for robust human-robot interaction
• Biomechanical modelling and analysis of human-coupled robotic systems
• Human-robot interaction applications in human-coupled robotic systems
• Cognitive human-robot collaboration applications for rehabilitation
Keywords: neural interface, cognitive human-robot interaction, human-robot collaboration, interactive learning and control, medical applications
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